A Review on Near-Duplicate Detection of Images using Computer Vision Techniques

Abstract

Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.

Visual link retrieval and knowledge discovery in painting datasets

“…It is worth noting that, when using a search query from a particular artist, other paintings from the same artist are excluded from the search, otherwise obvious, self links are likely to be retrieved. This is different from retrieving near-duplicate images (see, for example, [38] and [37]), since if the same artist did a different painting, this will not be retrieved as similar or nearsimilar work. The concept of near-duplicate image retrieval does not apply to the same artist in this paper, as the secondary goal of the proposed method is to find similarities among different artists, thus retrieving influences among them.…”

Defining a no-reference image quality assessment by means of the self-affine analysis

“…Specifically, in the medical imaging field these differences could make an important difference in the estimated Most Opinion Score. Then, the proposal can be tested in several image databases that meet these requirements, Thyagharajan in [13] analyzes several of these databases. With this, the number of Image Quality Assessment Algorithms should be expanded, such as the one proposed by Thyagharajan in [12], where many of these databases were used.…”

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

“…For example, such as the environmental microorganism classification [67], blood cell classification [109], classification for different types of microorganisms [76]. After that, the segmentation methods for microorganisms can be referred to by DIP researchers, such as stem cell segmentation [60], near-duplicate detection [113], image enhancement [96], cancer cell segmentation [23], environmental microorganism segmentation [122]. Moreover, microscopic image processing performs an essential role in industrial analysis, such as the monitoring for wastewater [7], beef carcass evaluation [32], monitoring of bacteria in milk [93], monitoring flames in an industrial boiler [119], softwood lumber grading [14] and so on.…”